Learning Meets Verification

Leucker, Martin

doi:10.1007/978-3-540-74792-5_6

Cited by 29 publications

(16 citation statements)

References 63 publications

(100 reference statements)

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“…The use of grammatical inference techniques for model construction in a verification context has been proposed in several papers (Cobleigh et al, 2003;Giannakopoulou and Pȃsȃreanu, 2005;Leucker, 2007;Singh et al, 2010;Feng et al, 2011). These papers focus on active learning using variants of L * , and only (Feng et al, 2011) considers the probabilistic case.…”

Section: Related Workmentioning

confidence: 99%

Learning deterministic probabilistic automata from a model checking perspective

et al. 2016

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. (2016). Learning deterministic probabilistic automata from a model checking perspective. Machine Learning, 105(2), 255-299. https://doi.org/10.1007/s10994-016-5565-9General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.? Users may download and print one copy of any publication from the public portal for the purpose of private study or research. ? You may not further distribute the material or use it for any profit-making activity or commercial gain ? You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access to the work immediately and investigate your claim. Abstract Probabilistic automata models play an important role in the formal design and analysis of hard-and software systems. In this area of applications, one is often interested in formal model-checking procedures for verifying critical system properties. Since adequate system models are often difficult to design manually, we are interested in learning models from observed system behaviors. To this end we adopt techniques for learning finite probabilistic automata, notably the Alergia algorithm. In this paper we show how to extend the basic algorithm to also learn automata models for both reactive and timed systems. A key question of our investigation is to what extent one can expect a learned model to be a good approximation for the kind of probabilistic properties one wants to verify by model checking. We establish theoretical convergence properties for the learning algorithm as well as for probability estimates of system properties expressed in linear time temporal logic and linear continuous stochastic logic. We empirically compare the learning algorithm with statistical model checking and demonstrate the feasibility of the approach for practical system verification.

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Section: Related Workmentioning

confidence: 99%

Learning deterministic probabilistic automata from a model checking perspective

et al. 2016

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“…For deterministic system models it has been suggested to use Angluin's [1] approach to learning deterministic finite automata [4], [11], [12]. For complex systems that are only partially observable via their interactions with the user, it can be quite unrealistic to assume that an adequate deterministic model exists.…”

Section: Introductionmentioning

confidence: 99%

Learning Probabilistic Automata for Model Checking

Mao

Chen

Jaeger

et al. 2011

2011 Eighth International Conference on Quantitative Evaluation of SysTems

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Abstract-Obtaining accurate system models for verification is a hard and time consuming process, which is seen by industry as a hindrance to adopt otherwise powerful modeldriven development techniques and tools. In this paper we pursue an alternative approach where an accurate high-level model can be automatically constructed from observations of a given black-box embedded system. We adapt algorithms for learning finite probabilistic automata from observed system behaviors. We prove that in the limit of large sample sizes the learned model will be an accurate representation of the data-generating system. In particular, in the large sample limit, the learned model and the original system will define the same probabilities for linear temporal logic (LTL) properties. Thus, we can perform PLTL model-checking on the learned model to infer properties of the system. We perform experiments learning models from system observations at different levels of abstraction. The experimental results show the learned models provide very good approximations for relevant properties of the original system.

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“…For more details, we refer the reader to the literature survey from Martin Leucker [18]. Pena et al propose to use learning for the purpose of state reduction in incompletely specified finite state machines [21].…”

Section: Related Workmentioning

confidence: 99%

Faster Statistical Model Checking by Means of Abstraction and Learning

Nouri

Raman

Bozga

et al. 2014

Runtime Verification

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International audienceThis paper investigates the combined use of abstraction and probabilistic learning as a means to enhance statistical model checking performance. We are given a property (or a list of properties) for verification on a (large) stochastic system. We project on a set of traces generated from the original system, and learn a (small) abstract model from the projected traces, which contain only those labels that are relevant to the property to be verified. Then, we model-check the property on the reduced, abstract model instead of the large, original system. In this paper, we propose a formal definition of the projection on traces given a property to verify. We also provide conditions ensuring the correct preservation of the property on the abstract model. We validate our approach on the Herman's Self Stabilizing protocol. Our experimental results show that (a) the size of the abstract model and the verification time are drastically reduced, and that (b) the probability of satisfaction of the property being verified is correctly estimated by statistical model checking on the abstract model with respect to the concrete system

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Learning Meets Verification

Cited by 29 publications

References 63 publications

Learning deterministic probabilistic automata from a model checking perspective

Learning deterministic probabilistic automata from a model checking perspective

Learning Probabilistic Automata for Model Checking

Faster Statistical Model Checking by Means of Abstraction and Learning

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